LangSmith and AgentOps: Elevating AI Agents Observability

What Is Agentic Observability and Why Does It Matter for AI Agents?

In today’s rapidly advancing AI landscape, agent-based systems—whether built on Large Language Models (LLMs) or traditional AI frameworks—are increasingly handling complex, autonomous tasks. As these systems take on critical roles in industries like manufacturing, finance, and logistics, the need for robust observability becomes more essential. Observability enables us to gain deep visibility into the inner workings of these agents, helping ensure their transparency, efficiency, and reliability. 

This blog explores LangSmith and AgentOps, two innovative platforms designed to deliver actionable insights into AI systems' operations. These platforms offer tools to monitor, analyze, and optimize the behavior of AI agents in real-time, making them indispensable for businesses relying on AI-driven solutions. 

Key Takeaways

1. Beyond traditional monitoring: Agentic Observability traces reasoning paths, tool calls, and decision workflows—not just system health metrics like uptime or latency

2. LangSmith for LLM pipelines: Provides end-to-end tracing of prompts, model responses, token usage, latency, and evaluation chains for language model applications

3. AgentOps for multi-agent systems: Monitors agent-to-agent communication, collaboration quality, resource allocation, and behavioral deviations in distributed agent architectures

4. Integration architecture: Unified observability through API ingestion, custom dashboards, anomaly detection, and real-time alerting—enabling centralized monitoring of heterogeneous agent systems

5. Critical business outcomes: Accelerated debugging (hours to minutes), performance optimization (identifying token waste, latency bottlenecks), and trust through transparent decision tracing

What is Observability in Agentic AI? 

Observability is essentially gaining insight into how an AI agent or a system of agents works internally, as used in agentic AI. That is far ahead from the traditional monitoring approach that tends to focus on external metrics-the kind of percentage of uptime of a system or network status-to understand how an AI agent or a system of agents functions. 

It involves gathering and analyzing data with agent behavior for interaction with environments or other agents, response behavior in answering queries posed to them, efficiency in processing workloads, and error-handling capabilities. Providing observability gives visibility into internal processes clearly giving a better view of how functionalities operate; thus, it supports easier debugging, performance optimization, and identification of potential failure points. 

Observability tools, such as LangSmith and AgentOps, go beyond simple logs and metrics. They offer actionable insights that help developers and operators optimize agent performance. 

Agentic Observability refers to the ability to monitor, trace, analyze, and explain the internal decision-making steps of AI agents, including LLM-based agents and multi-agent systems. Unlike traditional monitoring, Agentic Observability provides visibility into reasoning paths, tool calls, workflows, and interactions across agents. This enables developers to debug, optimize, and trust agentic systems at scale.

How is Agentic Observability different from traditional monitoring?

Traditional monitoring tracks system health; Agentic Observability tracks reasoning, tool usage, and decision paths.

Why Is Agentic Observability Essential for AI Systems?

Observability makes the working of AI agent systems transparent and accountable. Without it, one can hardly understand the "why" behind some decisions, especially in LLM or multi-agent environments. Debugging will be much harder because, even if errors happen, tracing back the roots of the error toward specific decision points will become cumbersome. 

Tools like Langsmith, push observability further into the LLMs so traces can be taken for the responses and hence easier to debug those problems on the part of the developer. AgentOps on the other side is for multi-agent systems, allowing teams to track collaboration, interaction, as well as the behavior of the agents. 

An AI Observability Platform is a unified system that captures logs, traces, metrics, prompts, outputs, tool calls, and decision paths from AI agents and LLM pipelines. Platforms like LangSmith and AgentOps deliver real-time visibility into performance, cost, reliability, and behavioral patterns. AI observability platforms help teams debug LLM outputs, analyze agent behavior, detect anomalies, and maintain trustworthy agentic systems.

• Intelligent Observability(also called Cognitive Observability) uses AI to interpret agent behavior, understand reasoning patterns, detect anomalies, and infer root-cause issues automatically. Instead of manually reviewing logs and traces, cognitive observability systems analyze hallucinations, degraded accuracy, faulty reasoning, and tool-call failures—providing predictive diagnostics that reduce debugging time.

• Agent-based Observability focuses on monitoring the decisions, actions, transitions, and collaborations of multiple autonomous agents. Tools like AgentOps track agent-to-agent communication, coordination quality, resource usage, and behavioral deviations. This helps developers diagnose bottlenecks, understand multi-agent dynamics, and optimize complex agent workflows.

• Observability with AI Agents means using AI-driven agents to automate and enhance observability workflows. Observability agents can summarize logs, analyze traces, detect anomalies, classify errors, and generate root-cause explanations automatically. This improves DevOps, SRE, and multi-agent reliability by enabling continuous intelligent oversight.

What does an AI Observability Platform provide?

It provides centralized monitoring of logs, reasoning traces, prompts, and performance metrics.

How Does It Differ from Traditional Monitoring?

Comparison: Traditional Monitoring vs Agentic Observability

Business Impact: Organizations using agentic observability reduce debugging time by 60-80%, improve model accuracy through prompt optimization, and maintain trust through explainable decision tracing.

What Are the Core Components of an AI Observability Platform?

1. Unified Data Ingestion Layer

Function: Capture heterogeneous data from distributed agent systems into centralized storage for analysis.

Data Sources:

• LLM interactions: Prompts, completions, token counts, latency, model metadata

• Tool invocations: API calls, function executions, database queries, external service requests

• Agent telemetry: State transitions, decision points, memory accesses, inter-agent messages

• Performance metrics: Response times, throughput, error rates, resource utilization

Implementation Pattern: Agents instrument their operations with OpenTelemetry-compatible tracers, sending structured logs and traces to the observability platform via APIs.

2. Trace and Reasoning Visualization

Challenge: AI agent workflows span multiple steps (planning → tool use → reasoning → action), creating complex execution graphs difficult to interpret from raw logs.

Solution: Visual trace representations showing:

• Execution timelines: Chronological view of operations with latency attribution

• Dependency graphs: Which steps depend on which tool outputs or agent responses

• Reasoning trees: Hierarchical visualization of goal decomposition and sub-task execution

• Multi-agent choreography: Message flows and coordination patterns across agents

LangSmith Example: Traces display a customer query → document retrieval → prompt construction → LLM reasoning → response generation pipeline, highlighting that 80% of latency occurs in document retrieval (not model inference).

3. Performance Analytics and Metrics

Key Metrics for LLM Systems:

• Token efficiency: Tokens per task, cost per operation, prompt compression opportunities

• Latency distribution: P50, P95, P99 response times across reasoning steps

• Accuracy metrics: Task completion rate, user satisfaction scores, error frequency

Model utilization: Which models handle which task types, switching logic effectiveness

Key Metrics for Multi-Agent Systems:

• Coordination efficiency: Message overhead, synchronization delays, deadlock frequency

• Load balancing: Task distribution across agents, idle time, bottleneck identification

• Collaboration quality: Success rate of delegated tasks, rework frequency

• Resource costs: Compute per agent, cost attribution to business operations

4. Anomaly Detection and Alerting

Anomaly Categories:

Reasoning Failures:

• Hallucinations (factually incorrect outputs)

• Logic errors (invalid inferences despite correct premises)

• Tool misuse (calling inappropriate APIs for given context)

Performance Degradation:

• Latency spikes beyond SLA thresholds

• Token consumption exceeding budget limits

• Error rate increases

Behavioral Deviations:

• Agents ignoring assigned goals

• Unexpected tool usage patterns

• Multi-agent deadlocks or infinite loops

Alerting Mechanisms: Real-time notifications via Slack, PagerDuty, or webhooks when anomalies exceed configured thresholds—enabling rapid incident response.

5. Evaluation and Testing Framework

Purpose: Systematically assess agent performance against benchmarks to validate improvements and detect regressions.

Evaluation Chains (LangSmith):

• Accuracy tests: Compare agent outputs to ground truth on curated datasets

• Consistency checks: Verify identical inputs produce consistent outputs across runs

• Adversarial testing: Probe with edge cases, ambiguous inputs, malicious prompts

Multi-Agent Simulations (AgentOps):

• Stress testing: Increase agent count or task load to identify scalability limits

• Fault injection: Simulate agent failures to test recovery and coordination robustness

• Scenario replay: Re-run historical traces to validate bug fixes

How Are LangSmith and AgentOps Implemented in Production Environments?

LangSmith Implementation 

LangSmith excels particularly in looking at how large language models work: it traces, logs, and analyzes all interactions between users and the model. This allows developers to monitor user queries and model responses and intermediate steps in real-time- and makes it easier to decide what doesn't quite meet output expectations. For example, if the customer service chatbot gives some wrong response, LangSmith enables the developer to go back through the whole conversation and can check- the input of the user or how the model has processed it, or even details like token usage and latency-all of them prove to be rich feeds about the model's efficiency and performance. 

How Developers Can Utilize LangSmith: 

• Logging Interactions: All user-model interaction instances will be logged automatically for analysis and review. 

• Performance Metrics: Model latency, token consumption, and execution time against which the model will be optimized in the continuous improvement process. 

• Error Debugging: Quickly identify incorrect outputs and errors which significantly helps in reducing troubleshooting time and enhancing the debugging process. 

• Evaluation Chains: It employs pre-defined evaluation chains that assess the performance of a model regarding assigned tasks, including precision and relevance.
  

AgentOps Implementation 

AgentOps addresses multi-agent systems where many AI agents interact and cooperate to achieve the goals of the systems. A good example might be a fleet of warehouse robots working cooperatively in synchronization to transport packages. AgentOps helps developers monitor the performance of each agent, identify bottlenecks in decision-making processes, and determine if there is one agent that's causing the overall inefficiency of the system. 

How Developers Can Utilize AgentOps: 

• Telemetry Data: It records fairly detailed data about the decisions by agents, transitions, and actions that give comprehensive insight into agent operation. 

• Behavioral Monitoring: Evaluate every decision by each of the agents based on what is supposed to be done by it and the tool executed. 

• Real-time Alerts: In case agents deviate from their assigned tasks or goals, real-time alerts are generated to correct the situation. 

• Collaboration Analysis: It analyses the quality of agent collaborations to make the agent interactions and overall performance better. 
  

 Architecture Diagrams and Explanations

Fig1: Architecture Diagram of Langsmith and AgentOps

• Core Workflow Engine: At the core of Akira AI lies its workflow engine, which is used to process every kind of data from LangSmith’s LLM observability to AgentOps multi-agent observability and aggregates logs, metrics, traces, and performance data. 

• Custom Dashboards: All observability metrics are displayed here, and can monitor the health and performance of LLM-based applications and multi-agent systems over time. 

• Metrics and Traces: This block stores and processes detailed performance data, including: 

• LLM Call Trace: Provides data on calls to the LLM APIs which, in turn, may allow tracing execution flow, failure detection, and model behaviors. 

• Cost Analysis: It tracks costs related to LLM calls, tracks resource utilization, and measures total operating costs. 

• Anomaly Detection & Alerts: Use the collected data to identify outliers or anomalous behavior related to agent activity or LLM responses to trigger alerts on some potential issues with the system. 
  

Running Applications

• LLM-based Application: LangSmith tracks interaction, token usage, and other performance metrics of an AI system based on large language models. 

• Multi-Agent System: An AI system in which multiple agents collaborate, monitored by AgentOps to analyze the pattern of interaction, the metrics of collaboration, and resource utilization. 
  

LangSmith and AgentOps Observability

• LangSmith constantly checks detailed statistics on LLM conversation traces, token usage, response times, and call traces. All such data is passed systematically through APIs to Akira AI for the central purpose of monitoring. 

• AgentOps tracks multi-agent interaction logs, collaboration, resource usage, and cost metrics. These insights flow into Akira AI for real-time analysis and optimization of agent workflows.